AQC0615

Nanopublication — Computational Image Analysis - AQC0615

by Arnaud Quercy · B minor - Research on Harmony · 2024

Claim 1: Computational Image Analysis - AQC0615

Analysis record [3]: B minor - Research [1] on Harmony (AQC0615) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-02-04.

Context

Analysis performed according to MMIDS-CMP-2025 [3] includes four metric categories: (1) Color distribution via k-means (10 colors), (2) Texture analysis using Haralick features, (3) Brightness and contrast measurements, (4) Spatial pattern characterization. Source image [5]: 2632x3510 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 33AB53 15.6 yellow-green mediumseagreen
2 90CD44 14.9 yellow-green yellowgreen
3 1E9642 14.3 yellow-green seagreen
4 ABDE5C 12.5 yellow-green ochre
5 73BC2A 9.1 yellow-green olivedrab
6 50C167 9.0 yellow-green limegreen
7 B5570E 7.7 orange chocolate
8 1A2D1E 7.5 yellow-green very dark green
9 C8F081 5.3 yellow-green khaki
10 74C2A8 4.2 green mediumaquamarine
11 837E52 0.3 yellow dimgray [Accent]
12 5D4D32 0.3 yellow-orange dark brown [Accent]

Color Families:

Family %
yellow-green 88.1
orange 7.7
green 4.2
yellow 0.3
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
837E52 yellow dimgray 25.5
5D4D32 yellow-orange dark brown 19.2

Texture Analysis

Metric Value
Global Roughness 0.178
Mean Local Roughness 0.028
Roughness Uniformity 0.021
Edge Density 0.18
Mean Gradient Magnitude 0.225
Gradient Variance 0.054
Gradient Smoothness 0.0
Directional Coherence 0.021
Pattern Complexity 0.119
Pattern Repetition 1.0
Detail Frequency Ratio 0.631
Spatial Variation 0.112
Texture Consistency 0.578

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.552
Brightness Variance 0.178
Brightness Uniformity 0.678
Brightness Skewness -0.52
Brightness Entropy 7.346
Rms Contrast 0.178
Michelson Contrast 1.0
Weber Contrast 0.523
Mean Local Contrast 0.03
Contrast Uniformity 0.305
Dynamic Range 1.0
Effective Dynamic Range 0.651
Shadow Percentage 8.014
Midtone Percentage 63.165
Highlight Percentage 28.821
Shadow Clipping 0.007
Highlight Clipping 0.0
Tonal Balance 0.091
Fine Contrast 0.016
Medium Contrast 0.037
Coarse Contrast 0.056
Multiscale Contrast Ratio 0.284
Edge Contrast 0.225
Contrast Clustering 0.422

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.745
Color Clustering 0.365
Color Transition Smoothness 0.458
Transition Uniformity 0.655
Sharp Transition Ratio 0.1
Transition Directionality 0.026
Mean Saturation 0.668
Saturation Variance 0.024
Low Saturation Ratio 0.014
Medium Saturation Ratio 0.55
High Saturation Ratio 0.437
Saturation Clustering 0.998
Hue Concentration 0.82
Complementary Balance 0.001
Analogous Dominance 0.9
Temperature Bias -0.405

Methodology

This analysis employs standardized computational methods for objective image characterization. Color extraction uses k-means clustering algorithm. Texture analysis applies Haralick feature extraction. Brightness metrics include mean, variance, and distribution analysis. Spatial patterns are characterized through coherence and clustering measurements. All methods are deterministic and reproducible. Analysis performed by Multimodal Institute's computational imaging systems.

References

[1] Arnaud Quercy (2024). B minor - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0615.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/b-minor-research-on-harmony_6ve.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/mmids-cmp-2025-computational-image-analysis-standard-dg1.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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